John Tortorella wants to know…

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John Tortorella by Robert Kowal. Licensed under Creative Commons via Commons.

Today, John Tortorella had a question about power plays.

It’s certainly a reasonable thing to wonder. It also seems like the kind of thing that, you know, your analytics person or department could quite easily figure out. But hey, the “let those Twitter guys figure it out” approach works as well I guess.

Anyway, I took the bait, partially because I am a power play fiend and partly because I’ve become more and more interested by faceoff impact. So I figured out the numbers. It’s important to note that NHL scorekeeping can be sketchy. Each scorer may have a different definition of a faceoff win, which can lead to some problems. But at this point, it’s what we have, so let’s take a look.

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A Look into Alex Ovechkin’s Elite Power Play Abilities

"Alex Ovechkin2" by Keith Allison. Licensed under Public Domain via Commons.

Alex Ovechkin2” by Keith Allison. Licensed under Public Domain via Commons.

I don’t know if we’ll ever see a power play quite like that of this decade’s Washington Capitals. We can’t attach a firm date to it because it could extend as far as the end of Alex Ovechkin’s career at this rate, but we know that its peak of power began with the hiring of Adam Oates as Caps head coach back in 2012. Oates had run a successful 1-3-1 power play for the New Jersey Devils with Ilya Kovalchuk as his trigger-man, but nothing even close to the heights he managed to achieve with the man advantage in his two seasons in DC. Barry Trotz, to his credit, has kept the same formation — what’s that old adage about things that ain’t broke? — with only minor tweaks, and last year the power play continued to succeed.

Now there’s a lot to discuss about the formation and its success — I like to think of the Caps’ PP as a work of art more than anything else — but for the sake of this post I’m going to focus in on Alex Ovechkin. Never has there been a more criticized future first-ballot Hall of Famer, nor arguably a more controversial elite goal scorer. It should already be a given that Ovechkin is the best power play goal scorer of all time — he sits fifth overall in PPG/g despite playing in a significantly lower scoring era than his contemporaries like Mike Bossy and Mario Lemieux — but I would argue by the time he retires, he will also likely be the greatest goal scorer of all time period. It’s the man advantage recently, in the latter stages of Ovechkin’s goal scoring peak, that has been the sniper’s bread and butter. Since Oates brought the 1-3-1 to town, Ovi has scored 48% of his goals on the power play, compared to 33% prior to that. He scored 25 power play goals last year, six ahead of the next highest total in Joe Pavelski’s 19. You have to go back another five to reach the player who is in third — Claude Giroux with 14 — indicating how great of a season the Sharks’ center/winger had, but that’s a story for another day.

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Exceeding Pythagorean Expectation: Part 5

“Bryz-warmup” by Arnold C. Licensed under Public Domain via Commons.

Bryz-warmup” by Arnold C. Licensed under Public Domain via Commons.

This is the fifth part of a five part series. Check out Part 1, Part 2, Part 3, Part 4 here. You can view the series both at Hockey-Graphs.com and APHockey.net.

To quickly recap what I’ve covered in the first four parts of this series, I have updated the work that’s been done on Pythagorean Expectations in hockey, and am looking to find out whether teams that have the best lead-protecting players are able to outperform those expectations consistently.

The first step is to figure out how to assess a player’s ability to protect leads. To do this, for every season, I isolated every player’s Corsi Against/60, Scoring Chances Against/60, Expected Goals Against/60 (courtesy of War-On-Ice) and Goals Against/60 when up a goal at even strength. I then found a team’s lead protecting ability for the year in question by weighting those statistics for each player by the amount of ice time they winded up playing that year. For players that didn’t meet a certain threshold, I gave them what I felt was a decent approximation of replacement level ability. For example, here was the expected lead protecting performance of the 2014-2015 Anaheim Ducks in each of those categories.

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Exceeding Pythagorean Expectations: Part 4

“Zdeno Chara 2012” by Sarah Connors. Licenced under Public Domain via Commons.

Zdeno Chara 2012” by Sarah Connors. Licensed under Public Domain via Commons.

This is the fourth part of a five part series. Check out Part 1, Part 2, Part 3, Part 5 here. You can view the series both at Hockey-Graphs.com and APHockey.net.

So now, four parts into this five part series, is probably a good time to discuss my original hypothesis and why I started this study. As I mentioned in my previous post, baseball has already gone through its Microscope Phase of analytics, where every broadly accepted early claim was put to the test to see whether it held up to strict scrutiny, and whether there were ways of adding nuance and complexity to each theory for more practical purpose. One of the first discoveries of this period was that outperforming one’s Pythagorean expectation for teams could be a sustainable talent — to an extent. Some would still argue that the impact is minimal, but it’s difficult to argue that it’s not there. What is this sustainable talent? Bullpens. Teams that have the best relievers, particularly closers, are more likely to win close games than those that don’t. One guess that I’ve heard put the impact somewhere around 1 win per season above expectations for teams with elite closers. That’s still not a lot, but it’s significant. My question would be, does such a thing exist in hockey?

Of course, there are no “closers,” strictly speaking. But there are players who close out games more often than others, and there are players whom coaches trust in late game lead-protecting situations more than others. Does a team with players who thrive in such situations have a higher chance of exceeding their expectation?

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Exceeding Pythagorean Expectations: Part 3

“Pythagorus Algebraic Separated” by John Blackburne. Licenced under Public Domain via Commons. The 2006 Red Wings may have been the best hockey team since the lost season.

Pythagorus Algebraic Separated” by John Blackburne. Licensed under Public Domain via Commons.

This is the third part of a five part series. Check out Part 1, Part 2, Part 4, Part 5 here. You can view the series both at Hockey-Graphs.com and APHockey.net.  

Since the last post was getting a little long, I decided to hold off on releasing the full Pythagorean results. Linked you will find a table of every team since the lost season, sorted by the difference between its adjusted point total and its Pythagorean expectation. Essentially, the teams that have the highest numbers in the right-most column are likely to have been the most fortunate, and those at the bottom were possibly unlucky. If you look at the 2014-2015 results below, you will see which teams should be a little bit worried about their chances, and those which may be ready for a rebound. Tomorrow, I will address what the point of this whole study was, and we’ll look at some more data.

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Exceeding Pythagorean Expectations: Part 2

“Pythagorus Algebraic Separated” by John Blackburne. Licenced under Public Domain via Commons. The 2006 Red Wings may have been the best hockey team since the lost season.

Pythagorus Algebraic Separated” by John Blackburne. Licensed under Public Domain via Commons.

This is the second part of a five part series. Check out Part 1, Part 3, Part 4, Part 5 here. You can view the series both at Hockey-Graphs.com and APHockey.net.

In Part 1, I looked at some of the theory behind Pythagorean Expectations and their origin in baseball. You can find the original formula copied below.

WPct = W/(W+L) = Runs^2/(Runs^2 + Runs Against^2)

The idea behind the formula is that it is a skill to be able to score runs and to be able to prevent them. What isn’t a skill, however — according to the theory — is when one scores or allows those runs. Teams over the course of weeks or months may appear to be able to score runs when they’re most necessary, to squeak out one-run wins, but as much as it looks like a pattern, it is most often simple variance. If you don’t fully buy into that idea, or you don’t really understand what I mean by variance, read this and then come back. Everything should be a lot clearer.

When applying Pythagorean Expectations to hockey, there are a couple of factors that complicate the matter. First of all, the goal/run scoring environment is very different. Hockey is a much lower scoring sport. That means that a team is more likely to win, say, 10 one-goal games in a row than in baseball. The lower the total goals, the closer the average scores, the more variance involved. Second, not all games are worth the same number of points. In baseball, you either win or lose, so you use run differential to figure out a winning percentage. But winning percentage doesn’t really work as a statistic in hockey since you can lose in overtime and get essentially half a win, while your opponent gets a full win.

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Exceeding Pythagorean Expectations: Part 1

Nashville Predators vs Detroit Red Wings, 18. April 2006” by Sean Russell. Licensed under Public Domain via Commons. The 2006 Red Wings may have been the best hockey team since the lost season.

This is the first part of a five part series.Check out Part 2, Part 3, Part 4, Part 5 here. You can view the series both at Hockey-Graphs.com and APHockey.net.

The 2015-2016 NHL season is almost here, and our sport has come upon a new phase — arguably the third — in its analytics progression. The first stage was about broad ideas and testing; I’ll call it the Discovery Phase. It involved public minds brainstorming large-scale ideas about the conventional truisms of the game, looking to prove and disprove that which many had taken for granted. It lent us ideas like the undervaluing of small players and terms like Corsi and PDO. It was revolutionary but not yet a revolution. The second phase was the Recognition Phase, which was kicked off by the Summer of Analytics. Teams began to buy into public work as worthy of investment and began to question their own practices. Now, as we saw it in baseball, a third phase is emerging. One in which much of the public is willing to accept the initially-controversial public ideas, but in which analysts are pushing back on generalities in situations that are often team and player dependent. We are now in a phase where analysts take a magnifying glass to every claim being made. For example, there is no more argument about whether or not Corsi is relevant or important — at least not among those in positions of influence. The question is in what cases it works best, and maybe more importantly, where and why it fails. Because it does, after all. There are players whose finishing abilities, defensive prowess, special teams impact and leadership mean that the value Corsi presents is significantly off base. And it’s important in a billion-dollar industry to figure out how to account for that. The same can be said for any of the metrics that came out of the Discovery Phase or that continue to be developed today.

The point of all this is that we’re at a point where you no longer dismiss the exceptions; you dig into them. There is a lot in the world that can be explained by simple variance, but the game of hockey is far too complicated to assign anything that doesn’t fit a successful model as such.

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Does Pace of Play Affect Shooting Percentages?

JP of Japers Rink had an interesting piece a while back about the idea of increasing pace of play. He explored the topic of whether a team should ever attempt to push the play or slow it down in order to give it the best chance of winning against a particular opponent.

Event rates are important because a 55% Corsi For Percentage is very different for a team that averages 110 Corsi events per game (for and against) compared to one that averages 90. The 2005-2006 Detroit Red Wings are an example of the former, the 2013-2014 New Jersey Devils of the latter. A team with a higher event rate with a positive shot attempt differential will end up on average with a better goal differential and likely a better record than one with a lower rate but the same differential.

The big question the piece raised for me, however, was whether pace of play can have an effect on shooting percentage. After all, we know that the score can affect shooting percentage based on the change in a team’s tactics and mindset. Is there a shooting-related reason why high event hockey might not be preferable?

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Why Splitting Back-To-Backs Between Goalies May Not Always Be The Right Call

There’s an important difference between always taking the middle ground in an argument and recognizing nuance where many find none. Analytics are a case in which it is important to remember, whether it’s with corsi, or PDO, or fighting, or any other issue, that because of the imperfection of our metrics, our understanding of psychological factors at play, and our understanding of just what goes on behind closed doors, that what the numbers tell you isn’t always entirely accurate. This nuance is something that I’ve tried to emphasize with this blog over the past few months, and will continue to push. There isn’t a middle ground just because somebody says there should be…there’s a middle ground because of the number of factors in play that simply haven’t been taken into account by any model we have at our disposal right now.

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